Software for financial services
In financial services the tolerance for being approximately right is zero, and every figure has to be explainable to someone who will ask. That shapes the engineering: correctness, traceability, and reconciliation come before speed of delivery.
Month-end assembled by hand
The reporting cycle consumes several days of skilled time in exports, spreadsheet manipulation, and manual reconciliation — a process that exists only in the head of the person who does it and produces figures nobody can fully trace back. It works, until that person is unavailable or a regulator asks how a number was derived.
You’ll recognise this if
- Period close depends on one person and a complex spreadsheet
- Reconciling two systems is a recurring manual job
- Producing a regulatory return takes days of skilled time
- Extracting data from documents is done by hand at volume
What we actually deliver
Reconciliation that runs itself
Automated matching between systems with exceptions surfaced for human judgement, rather than a person scanning two reports for differences. The exceptions are the work; the matching should not be.
Reporting with a traceable lineage
Every figure traceable back through each transformation to the source records. When someone asks how a number was arrived at, the answer is a link rather than an investigation.
Document processing at volume
Extraction from statements, contracts, and forms, with confidence scores and human review on anything uncertain — a genuinely strong application of current AI, provided the review path is designed in.
Controls and audit built in
Segregation of duties, approval workflows, immutable audit trails, and retention handled as part of the data model rather than bolted on to satisfy a review.
The way we approach it
Correctness over throughput
We would rather a process be slower and provably right. In this sector a fast wrong answer is not a partial success — it is the failure mode.
Run in parallel before switching
New calculations run alongside the existing process until the outputs agree over several cycles. Cutting over on the strength of a passing test suite is not enough here.
Keep a human on consequential decisions
Automation belongs on the volume; judgement stays with a person, given the context to exercise it properly. Where AI is involved, the review path is designed before the model is.
What changes
- Period close measured in hours rather than days
- Every figure traceable to its source records
- Reconciliation exceptions surfaced instead of hunted for
- Controls that stand up to an audit without a scramble
Asked often enough to answer here
Can AI be used on regulated financial processes?
In document extraction and drafting, commonly and effectively — with confidence thresholds and human review on anything uncertain. For decisions affecting customers, explainability requirements usually point toward conventional models rather than generative ones, and we will tell you which side a given use case falls on.
How do you handle data residency and access?
As a design constraint from the start. Where data cannot leave a jurisdiction or your own infrastructure, that shapes the architecture rather than being handled by an exception at the end.
Do you have regulatory expertise?
We have engineering experience with the controls these regimes require — audit trails, segregation of duties, lineage, retention. We are not your compliance function and do not pretend to be; we build software your compliance team can sign off and evidence.
Related
Wherever you’re starting from, let’s figure out the next step.
Tell us what you’re building — we’ll tell you honestly whether we’re the right team for it.